Parallel, angular and perpendicular parking for self-driving cars using deep reinforcement learning

The progress in creating a fully autonomous selfdriving car has steadily increased in recent decades. Consequently, autonomous parking has been a well-researched field since every driving trip must end with a parking manoeuvre. In recent years, with the current successes in reinforcement learning, the concept of applying it to solve the autonomous parking problem has been more and more explored. A vehicle equipped with a complete autonomous parking system must perform three types of parking: perpendicular, angular and parallel parking. Autonomous parking systems control the steering angle and the vehicle speed by considering the surrounding space conditions to ensure collision-free motion within the available space. This paper presents an approach to the problem of autonomous parking using Reinforcement Learning, more precisely, Deep Deterministic Policy Gradient. This approach proved to be capable of parking in a variety of different environments for the three parking manoeuvres.This work has been supported by FCT-Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. In addition, this work has also been funded through a doctoral scholarship from the Portuguese Foundation for Science and Technology (Fundacao para a Ciencia e a Tecnologia) [grant number SFRH/BD/06944/2020], with funds from the Portuguese Ministry of Science, Technology and Higher Education and the European Social Fund through the Programa Operacional do Capital Humano (POCH)

Non-Causal Autonomous Parking System for Driverless Vehicles

According to an Audi Urban Future Initiative study, the average person spends 106 days over their life-time searching for parking spaces. Whether it is on the side of a busy city street or a shopping center car park, the issue of parking private vehicles poses a substantial logistical challenge that scales in complexity along with population density. As modern populations trend towards urbanization it becomes imperative to develop more efficient parking structures. With the inevitable shift towards driverless vehicles, there exists a need to establish a control system to mitigate these complications. One embodiment of such a solution is a distributed sensor network feeding real-time data to a central management system which delegates navigational directives to individual vehicles based on algorithms designed to maximize spatial and temporal efficiency. This method would rely on wireless radio communication between the host and client nodes with a static sensor providing state feedback information enabling a non-causal autonomous parking process. The project strives to streamline the process of finding a vacant parking space while ensuring client safety through the direction of localized traffic by means of an optimized control scheme determined by the central server leveraging data collected from the sensor network. Such a mechanism would not only improve safety and efficiency by reducing collisions and time spent searching for open spaces, but also obviate the need for driverless vehicles to have prior knowledge of the destination layout by having the information available locally and on demand.https://scholarscompass.vcu.edu/capstone/1038/thumbnail.jp

Reinforcement Learning for Autonomous Parking

Tato diplomová práce se zabývá aplikací reinforcement learning metod na úlohy autonomního parkování. Práce se zaměřuje na implementaci simulačního parkovacího prostředí pro testování autonomních agentů s využitím herního engine Unity a knihovny ML-Agents. Práce nejprve poskytuje přehled reinforcement learning metod, včetně deep reinforcement learning algoritmu Proximal Policy Optimization a imitation learning algoritmu Generative Adversarial Imitation Learning. Dále jsou diskutovány současné technologie a přístupy v oblasti autonomního parkování. Následuje implementace simulačního prostředí a specifikace procesu odměňování a trénování autonomních agentů. Výsledky experimentů ukazují efektivitu reinforcement learning přístupů na úlohy autonomního parkování v různých scénářích, včetně fixních a náhodných cílů a paralelního parkování.This master thesis explores the application of reinforcement learning methods to autonomous parking tasks. The thesis focuses on the implementation of a parking simulation environment for testing autonomous agents using the Unity game engine and the ML-Agents library. The thesis first provides an overview of reinforcement learning methods, including deep reinforcement learning algorithm Proximal Policy Optimization and imitation learning algorithm Generative Adversarial Imitation Learning. The current state-of-the-art technologies and approaches in autonomous parking are also discussed, followed by the implementation of the simulation environment and the specification of the rewards and training process of autonomous agents. The experimental results demonstrate the effectiveness of reinforcement learning based approaches to autonomous parking tasks in various scenarios, including fixed and random targets and parallel parking.460 - Katedra informatikyvýborn

A Spatial and Operations Modeling Method for Automatic Parking Systems

Automatic or Automated Parking Systems (APSs) would have spatial configurations that depend on both or any of the following factors: 1) its operations and 2) the physical structure design. Without a modeling method, designing and configuring an APS could be relatively challenging and result to special instances of APSs. On the other hand, a modeling method for this purpose would facilitate representing the spaces and operations used by APSs thus aid their design and configuration, and possibly make them adaptive to physical space constraints. This study developed such method for modeling the spaces and operations of APSs allowing their design and configuration to be highly flexible. It involved defining an approach for spatial representations and establishing a model for representing the operations of autonomous parking devices of APSs. The implementation of the spatial representations and operations model into a data structure suitable for computer programming was also described. A number of configuration examples based on those offered by current APS service providersand a few hypothetical APS designs were used to test the applicability of the method. The test was facilitated by simulation software that allowed input of varied APS configurations, input of basic car parking and retrieval operations, and showing results of such operations. Results show that basic operations are correctly executed thus indicating that the model is applicable. Keywords: modeling method, Automatic Parking Systems (APS), autonomous parking devices, spatial model, parking device operations mode

Optimization-Based Motion Planning for Autonomous Parking Considering Dynamic Obstacle: A Hierarchical Framework

We present a hierarchical framework based on graph search and model predictive control (MPC) for electric autonomous vehicle (EAV) parking maneuvers in a tight environment. At high-level, only static obstacles are considered, and the scenario-based hybrid A* (SHA*), which is faster than the traditional hybrid A*, is designed to provide an initial guess (also known as a global path) for the parking task. To extract the velocity and acceleration profile from an initial guess, an optimal control problem (OCP) is built. At the low level, an NMPC-based strategy is used to avoid dynamic obstacles (also known as local planning). The efficacy of SHA* is evaluated through 148 different simulation schemes and the proposed hierarchical parking framework is demonstrated through a real-time parallel parking simulation